llama-stack-mirror/llama_toolchain/cli/distribution/configure.py
2024-08-08 10:13:38 -07:00

106 lines
3.7 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import argparse
import json
import shlex
import yaml
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.common.config_dirs import DISTRIBS_BASE_DIR
from termcolor import cprint
class DistributionConfigure(Subcommand):
"""Llama cli for configuring llama toolchain configs"""
def __init__(self, subparsers: argparse._SubParsersAction):
super().__init__()
self.parser = subparsers.add_parser(
"configure",
prog="llama distribution configure",
description="configure a llama stack distribution",
formatter_class=argparse.RawTextHelpFormatter,
)
self._add_arguments()
self.parser.set_defaults(func=self._run_distribution_configure_cmd)
def _add_arguments(self):
self.parser.add_argument(
"--name",
type=str,
help="Name of the distribution to configure",
required=True,
)
def _run_distribution_configure_cmd(self, args: argparse.Namespace) -> None:
from llama_toolchain.distribution.datatypes import DistributionConfig
from llama_toolchain.distribution.registry import resolve_distribution_spec
config_file = DISTRIBS_BASE_DIR / args.name / "config.yaml"
if not config_file.exists():
self.parser.error(
f"Could not find {config_file}. Please run `llama distribution install` first"
)
return
# we need to find the spec from the name
with open(config_file, "r") as f:
config = DistributionConfig(**yaml.safe_load(f))
dist = resolve_distribution_spec(config.spec)
if dist is None:
raise ValueError(f"Could not find any registered spec `{config.spec}`")
configure_llama_distribution(dist, config)
def configure_llama_distribution(dist: "Distribution", config: "DistributionConfig"):
from llama_toolchain.common.exec import run_command
from llama_toolchain.common.prompt_for_config import prompt_for_config
from llama_toolchain.common.serialize import EnumEncoder
from llama_toolchain.distribution.dynamic import instantiate_class_type
python_exe = run_command(shlex.split("which python"))
# simple check
conda_env = config.conda_env
if conda_env not in python_exe:
raise ValueError(
f"Please re-run configure by activating the `{conda_env}` conda environment"
)
if config.providers:
cprint(
f"Configuration already exists for {config.name}. Will overwrite...",
"yellow",
attrs=["bold"],
)
for api, provider_spec in dist.provider_specs.items():
cprint(f"Configuring API surface: {api.value}", "white", attrs=["bold"])
config_type = instantiate_class_type(provider_spec.config_class)
provider_config = prompt_for_config(
config_type,
(
config_type(**config.providers[api.value])
if api.value in config.providers
else None
),
)
print("")
config.providers[api.value] = {
"provider_id": provider_spec.provider_id,
**provider_config.dict(),
}
config_path = DISTRIBS_BASE_DIR / config.name / "config.yaml"
with open(config_path, "w") as fp:
dist_config = json.loads(json.dumps(config.dict(), cls=EnumEncoder))
fp.write(yaml.dump(dist_config, sort_keys=False))
print(f"YAML configuration has been written to {config_path}")